The present disclosure relates to a learning device, a learning method, an identification device, an identification method, and a program. In particular, the disclosure relates to a learning device, a learning method, an identification device, an identification method, and a program capable of more promptly and more accurately identifying whether or not the subjects appearing in two images are the same subject.
Face identification methods in related art are roughly classified into two methods of an identification method using a same-individual classifier and an identification method using a multi-class classifier. The identification method using the same-individual classifier is disclosed in “Face Recognition Using Boosted Local Features” (Michael Jones and Paul Viola, Mitsubishi Electric Research Laboratories Technical Report, April 2003) and “Face Recognition Using Ada-Boosted Gabor Features” (P. Yang, S. Shan, W. Gao, S. Li, D. Zhang, International Conference on). The identification using the same-individual classifier is performed, for example, by calculating a difference between features extracted from two face images and using the difference as an input of the same-individual classifier.
The technique disclosed in “Face Recognition Using Boosted Local Features” is considered to be weak against changes in environment such as change in illumination in that a feature difference between correspondence points of two images is performed. Further, likewise, the technique disclosed in “Face Recognition Using Ada-Boosted Gabor Features” is also considered to be weak against changes in environment in that the same calculation processing is performed by using a filter different from a filter disclosed in “Face Recognition Using Boosted Local Features”.
Accordingly, in order to solve the problem of weakness against environmental change, there is proposed a technique disclosed in Japanese Unexamined Patent Application Publication No. 2008-165731. FIG. 1 is a diagram illustrating a flow of face identification in related art disclosed in Japanese Unexamined Patent Application Publication No. 2008-165731.
In the apparatus disclosed in Japanese Unexamined Patent Application Publication No. 2008-165731, at the time of identification, as indicated by the tips of the arrows #1 and #2, features are extracted from the respective feature points of an input image by using a plurality of Gabor filters, and a feature vector, of which a parameter is set as the feature extracted by using each Gabor filter, is calculated for each feature point.
FIG. 2 is a diagram illustrating a Gabor filter. The characteristic of the Gabor filter is defined by the size and the direction of the fringe portion. In the apparatus disclosed in Japanese Unexamined Patent Application Publication No. 2008-165731, a predetermined number of filters are selected among 40 different filters, of which the characteristics are defined by 5 different sizes and 8 different orientations, in accordance with the positions of the feature points, and are used in extraction of the features at the respective feature points.
In the apparatus disclosed in Japanese Unexamined Patent Application Publication No. 2008-165731, the correlation between the feature vectors, which are calculated from the same feature points of two images, can be calculated as indicated by the tip of the outlined arrow #11. Further, as indicated by the tip of the arrow #12, the correlation vector, of which a parameter is the correlation coefficient of the feature vector representing the feature of the same feature point, is used as an input of the classifier, thereby determining whether or not the individuals are the same.
According to the technique disclosed in Japanese Unexamined Patent Application Publication No. 2008-165731, all the 40 different filters are not used in the feature extraction, but several filters are used therein by combining the outputs of the filters. Thus, it is possible to improve precision in identification.